import FinanceDatabase as fd
all_technology_companies = fd.select_equities(sector='Technology')
silicon_valley = fd.search_products(all_technology_companies,
query='San Jose',
search='city')
for ticker in silicon_valley.copy():
if '.' in ticker:
del silicon_valley[ticker]
import FundamentalAnalysis as fa
API_KEY = "YOUR_API_KEY_HERE"
data_set = {}
for ticker in silicon_valley:
try:
data_set[ticker] = fa.key_metrics(ticker, API_KEY, period='annual')
except Exception:
continue
import pandas as pd
import matplotlib.pyplot as plt
years = ['2016', '2017', '2018', '2019', '2020']
market_cap = pd.DataFrame(index=years)
for ticker in data_set:
try:
data_years = []
for year in years:
data_years.append(data_set[ticker].loc['marketCap'][year])
import FinanceDatabase as fd
import pandas as pd
core_selection = fd.select_etfs("core_selection_degiro_filtered")
tickers = pd.Series(core_selection.keys())
tickers.to_excel('core_selection_tickers.xlsx', index=None, header=None)
import FinanceDatabase as fd
from yfinance.utils import get_json
from yfinance import download
import matplotlib.pyplot as plt
airlines_us = fd.select_equities(country='United States', industry='Airlines')
airlines_us_fundamentals = {}
for symbol in airlines_us:
airlines_us_fundamentals[symbol] = get_json(
"https://finance.yahoo.com/quote/" + symbol)
airlines_us_stock_data = download(list(airlines_us))
for symbol in airlines_us_fundamentals:
quick_ratio = airlines_us_fundamentals[symbol]['financialData'][
'quickRatio']
long_name = airlines_us_fundamentals[symbol]['quoteType']['longName']
if quick_ratio is None:
continue
plt.barh(long_name, quick_ratio)
plt.tight_layout()
plt.show()
import FinanceDatabase as fd
import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf
from ta.volatility import BollingerBands
# Obtain all ETFs in the category health then filter based on biotech
health_etfs = fd.select_etfs(category='Health')
health_etfs_in_biotech = fd.search_products(health_etfs, 'biotech')
# Download stock data on the Biotech companies
stock_data_biotech = yf.download(list(health_etfs_in_biotech.keys()),
start="2020-01-01",
end="2020-06-01")['Adj Close']
stock_data_biotech = stock_data_biotech.dropna(axis='columns')
# Initalise the plot and row/column variables
figure, axis = plt.subplots(4, 3)
row = 0
column = 0
# Loop over the tickers
for ticker in stock_data_biotech.columns:
# Initalise the DataFrame
data_plot = pd.DataFrame(stock_data_biotech[ticker])
# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=stock_data_biotech[ticker],
window=20,
window_dev=2)
import FinanceDatabase as fd
from yfinance.utils import get_json
import matplotlib.pyplot as plt
all_etfs = fd.select_etfs()
semiconductor_etfs = fd.search_products(all_etfs, 'semiconductor')
# Remove some unwanted ETFs
del semiconductor_etfs['DXSH.DE']
del semiconductor_etfs['DXSH.F']
semiconductor_etfs_fundamentals = {}
for symbol in semiconductor_etfs:
semiconductor_etfs_fundamentals[symbol] = get_json(
"https://finance.yahoo.com/quote/" + symbol)
for symbol in semiconductor_etfs_fundamentals:
ytd_return = semiconductor_etfs_fundamentals[symbol]['fundPerformance'][
'trailingReturns']['ytd']
long_name = semiconductor_etfs_fundamentals[symbol]['quoteType'][
'longName']
if ytd_return is None:
continue
plt.barh(long_name, ytd_return)
plt.tight_layout()
plt.xticks([-1, -0.5, 0, 0.5, 1], ['-100%', '-50%', '0%', '50%', '100%'])
plt.show()
import FinanceDatabase as fd
import matplotlib.pyplot as plt
# Obtain all countries from the database
equities_countries = fd.show_options('equities', 'countries')
# Obtain all sectors from the database
equities_sectors = fd.show_options('equities', 'sectors')
# Obtain all industries from the database
equities_industries = fd.show_options('equities', 'industries')
# Obtain all industries from the database
equities_all_categories = fd.show_options('equities')
equities_per_sector_netherlands = {}
for sector in equities_sectors[1:]: # I skip the first sector since it means 'No Category'
try:
equities_per_sector_netherlands[sector] = len(fd.select_equities(country='Netherlands', sector=sector))
except ValueError as error:
print(error)
legend, values = zip(*equities_per_sector_netherlands.items())
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k', 'tab:blue', 'tab:orange', 'tab:gray',
'lightcoral', 'yellow', 'saddlebrown', 'lightblue', 'olive']
plt.pie(values, labels=legend, colors=colors,
wedgeprops={'linewidth': 0.5, 'edgecolor': 'white'})
plt.title('Companies per sector in the Netherlands')
plt.tight_layout()
def search_products(self, product):
return fd.search_products()
def show_options(self, product):
'''
Can use this for initial tree of display options
'''
return fd.show_options(product)